{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 绘制位置与时间关系图\n",
    "在这个 notebook 中，你需要绘制一个刚才看到的数据中位置与时间的关系图。\n",
    "\n",
    "首先，我将通过以下步骤演示这个绘图过程：\n",
    "\n",
    "1. 导入Python最流行的绘图库 `pyplot`。\n",
    "2. 将数据存储在名为`X`和`Y`的变量中。\n",
    "3. 使用pyplot的`scatter()`函数创建一个该数据的散点图。\n",
    "4. 使用pyplot的`plot()`函数添加一个连接两个数据点的线。\n",
    "5. 为该图表添加轴标签与一个标题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1.\n",
    "#  we import pyplot as plt so we can refer to the pyplot\n",
    "#  succinctly. This is a standard convention for this library.\n",
    "\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "刚开始，我只告诉你2：00与3：00时的里程数，该数据看起来是这样的。\n",
    "\n",
    "|  时间  | 里程数 <br>(英里) |\n",
    "| :--: | :----------: |\n",
    "| 2:00 |      30      |\n",
    "| 3:00 |      80      |\n",
    "\n",
    "我想要制作一个这些数据的散点图，并且希望**横**轴显示时间，**纵**轴显示里程数。\n",
    "\n",
    "在这个notebook（以及后面的notebook）中，我们将使用大写字母`X` 来存储横轴数据，并使用大写字母`Y`来存储纵轴数据。在这个示例中："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2.\n",
    "#  get the data into variables called X and Y. This naming pattern\n",
    "#  is a convention. You could use any variables you like.\n",
    "\n",
    "X = [2,3]\n",
    "Y = [30,80]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wH/CaQ576blYlWQq8kMEy00gdt4HerS/dCOyqqj85zLDbgbd0V7tcBHy5qh5ftCJHpE+vSc4GtgJvnuRZG/Trt6rOqarVVbWawZrjW6vqtkUsc2R6fi9vA342ydIkJwM/xWDteeL07PdRBr+NkOQMYA3wyOJUOFpJprqZOUmWA5cCnzpk2O3Axm77MuCe6v5COkrH85LLxcCbgZ3d2hQM/jJ+NkBVfYDB1Q+vAx4GvsHgp/4k6tPr7zNYc3t/twSxvyb3nxz16bclQ/utql1J7gA+AXwbuOGQX9knSZ+v7x8Cf5VkJ4PliHd2v5lMopXAliRLGEySb66qDyf5A2Cmqm5n8APub5I8zGBmfvk4CvGdopLUiON2yUWStDAGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5Jjfh/dKkEErKTz6YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 3.\n",
    "#  create a scatter plot using plt.scatter. Note that you NEED\n",
    "#  to call plt.show() to actually see the plot. Forgetting to  \n",
    "#  call plt.show() is a common source of problems for people \n",
    "#  new to this library\n",
    "\n",
    "plt.scatter(X,Y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个散点图看起来不那么令人激动，因为它只有两个数据点。让我们再添加一条连接这些数据点的线。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 4.\n",
    "#  add lines connecting adjacent points\n",
    "\n",
    "plt.scatter(X,Y)\n",
    "plt.plot(X,Y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们为X轴和Y轴添加一个标题和标签。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X,Y)\n",
    "plt.plot(X,Y)\n",
    "plt.title(\"Position vs. Time on a Roadtrip\")\n",
    "plt.xlabel(\"Time (in hours)\")\n",
    "plt.ylabel(\"Odometer Reading (in miles)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 时间间隔：二十分钟\n",
    "每*20*分钟查看一次里程表，其数据如下所示：\n",
    "\n",
    "|  时间  | 里程数<br>(英里) |\n",
    "| :--: | :---------: |\n",
    "| 2:00 |     30      |\n",
    "| 2:20 |     40      |\n",
    "| 2:40 |     68      |\n",
    "| 3:00 |     80      |\n",
    "\n",
    "但这里有一个更好的绘图方法，具体是这样的（注意时间表示上的差异）：\n",
    "\n",
    "|  时间   | 里程数<br>(英里) |\n",
    "| :---: | :---------: |\n",
    "| 2.000 |     30      |\n",
    "| 2.333 |     40      |\n",
    "| 2.667 |     68      |\n",
    "| 3.000 |     80      |\n",
    "\n",
    "### 练习1 - 使用连接相邻点的线，绘制一个上述数据中的位置与时间关系图。\n",
    "\n",
    "使用上面显示的数据重现这个过程。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO - your code for exercise 1 here\n",
    "from matplotlib import pyplot as plt\n",
    "X = [\n",
    "    2.000,\n",
    "    2.333,\n",
    "    2.667,\n",
    "    3.000\n",
    "]\n",
    "\n",
    "Y = [\n",
    "    30,\n",
    "    40,\n",
    "    68,\n",
    "    80\n",
    "]\n",
    "plt.scatter(X,Y)\n",
    "plt.plot(X,Y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 练习1 - 解决方案检查（notebook末尾有完整的解决方案代码）\n",
    "如果生成了一个如下所示的图表，你的绘图就是正确的：\n",
    "\n",
    "![](https://d17h27t6h515a5.cloudfront.net/topher/2017/December/5a2ee74f_vmc-l1-20-min-plot/vmc-l1-20-min-plot.png)\n",
    "\n",
    "\n",
    "### 练习2 - 思考题\n",
    "看一看上面的图表，并思考以下问题（我们将在下面的视频中进一步讨论这些问题）\n",
    "\n",
    "1. 在不查看实际数据的情况下，你如何知道哪些时间间隔的平均速度最高？\n",
    "\n",
    "2. 在一个3:00 - 4:00期间停止运行的汽车里，你要将这些数据绘制成图，则那条线的**斜率**是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# \n",
    "\n",
    "#\n",
    "\n",
    "#\n",
    "\n",
    "#  SOLUTION CODE BELOW\n",
    "\n",
    "#\n",
    "\n",
    "#\n",
    "\n",
    "#\n",
    "\n",
    "# Exercise 1 - Solution\n",
    "\n",
    "X = [\n",
    "    2.000,\n",
    "    2.333,\n",
    "    2.667,\n",
    "    3.000\n",
    "]\n",
    "\n",
    "Y = [\n",
    "    30,\n",
    "    40,\n",
    "    68,\n",
    "    80\n",
    "]\n",
    "\n",
    "plt.scatter(X,Y)\n",
    "plt.plot(X,Y)\n",
    "plt.title(\"Position vs. Time on a Roadtrip\")\n",
    "plt.xlabel(\"Time (in hours)\")\n",
    "plt.ylabel(\"Odometer Reading (in miles)\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
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 "nbformat": 4,
 "nbformat_minor": 2
}
